Summary of Estimating the Probabilities Of Rare Outputs in Language Models, by Gabriel Wu et al.
Estimating the Probabilities of Rare Outputs in Language Models
by Gabriel Wu, Jacob Hilton
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning paper investigates the challenge of estimating the probability of a rare binary outcome from a model’s output. The problem arises when random sampling is insufficient due to the rarity of the event. The researchers study this issue in the context of transformer language models and compare two approaches: importance sampling, which involves finding inputs that produce the desired output, and activation extrapolation, which extrapolates a probability distribution from the model’s logits. The results show that importance sampling outperforms activation extrapolation and naive sampling. The paper also discusses how minimizing the probability estimate of an undesirable behavior generalizes adversarial training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new machine learning paper tries to solve a tricky problem: estimating how likely it is for a certain event to happen when you get output from a model. This is important because sometimes this event might not be very common, so just guessing won’t give us the right answer. The scientists looked at this issue in language models and tried two different methods. One method involves finding specific inputs that would produce the rare outcome, while the other method tries to predict how likely it is based on what the model thinks about each input. They found out that the first method works better than the second one and just guessing. This research can help us make sure our models don’t have bad behavior when things go wrong. |
Keywords
» Artificial intelligence » Logits » Machine learning » Probability » Transformer
